High-content synaptic phenotyping in human cellular models reveals a role for BET proteins in synapse assembly

Resolving fundamental molecular and functional processes underlying human synaptic development is crucial for understanding normal brain function as well as dysfunction in disease. Based upon increasing evidence of species-divergent features of brain cell types, coupled with emerging studies of complex human disease genetics, we developed the first automated and quantitative high-content synaptic phenotyping platform using human neurons and astrocytes. To establish the robustness of our platform, we screened the effects of 376 small molecules on presynaptic density, neurite outgrowth, and cell viability, validating six small molecules that specifically enhanced human presynaptic density in vitro. Astrocytes were essential for mediating the effects of all six small molecules, underscoring the relevance of non-cell-autonomous factors in synapse assembly and their importance in synaptic screening applications. Bromodomain and extraterminal (BET) inhibitors emerged as the most prominent hit class and global transcriptional analyses using multiple BET inhibitors confirmed upregulation of synaptic gene expression. Through these analyses, we demonstrate the robustness of our automated screening platform for identifying potent synaptic modulators, which can be further leveraged for scaled analyses of human synaptic mechanisms and drug discovery efforts.


Introduction
Synapses are intercellular junctions between neurons crucial for information processing. Seminal studies using the frog neuromuscular junction (del Castillo and Katz, 1954) or giant squid synapse (Bullock and Hagiwara, 1957) established fundamental principles of chemical transmission. Subsequent genetic and neurobiological studies have further illuminated our understanding of the mammalian central nervous system and have implicated synaptic alterations in brain disorders, including autism spectrum disorder (Bourgeron, 2015;De Rubeis et al., 2014), schizophrenia Kirov et al., 2012;Purcell et al., 2014), and Alzheimer's disease (Kunkle et al., 2019;Spires-Jones and Hyman, 2014). Thus, insights into synaptic development and function are critical for our understanding of normal brain function as well as dysfunction in disease. Diverse genes such as neuroligins and neurexins (Graf et al., 2004;Varoqueaux et al., 2006), neurotrophic receptors (Elmariah et al., 2004), EphrinB and EphrinB receptors (Henderson and Dalva, 2018), and cadherins, protocadherins, and catenins (Arikkath and Reichardt, 2008;Weiner et al., 2005;Yamagata et al., 2018) have been implicated in synaptic biology, although given the complex nature of mammalian brain development and function, molecular programs underlying neuronal connectivity have yet to be fully resolved.
Notably, most synaptic studies in mammals use rodent primary neuronal cultures (Linhoff et al., 2009;Nieland et al., 2014;Sharma et al., 2013;Sharma et al., 2012;Verstraelen et al., 2014). While the general principles of neuronal and synaptic development are thought to be conserved across species (DeFelipe, 2011), increasing evidence suggests that species-divergent features influence signal processing in dendrites and axons, which in turn may contribute to differences in cognition and function across organisms (Eyal et al., 2016;Molnár et al., 2016). Indeed, it is possible that human-specific aspects of synapse biology contribute to higher order cognition and function and may be perturbed in disease, underscoring the importance of utilizing both highly tractable, well-studied animal models as well as emerging human cellular in vitro models to identify and analyze convergent and divergent principles. In this regard, studies comparing human and rodent cortical neurons have revealed differences in membrane capacitance (Eyal et al., 2016), epigenetic signatures (Luo et al., 2017), gene expression and transcriptional regulatory mechanisms (Bakken et al., 2016;Hodge et al., 2019;Zeng et al., 2012), as well as changes in activity-dependent gene expression (Pruunsild et al., 2017) associated with distinct somatic, axonal, dendritic, and spine morphologies (Beaulieu-Laroche et al., 2018;Benavides-Piccione et al., 2002;Mohan et al., 2015), which correlate with differences in synaptic plasticity (Szegedi et al., 2016;Verhoog et al., 2013) and likely contribute to the differences observed in the integrative properties of the neuron (Molnár et al., 2016;Szegedi et al., 2016;Verhoog et al., 2013). For example, electrophysiological analyses have revealed key differences in spike timing-dependent plasticity in the human cortex compared with rodent, including the ability of human synapses to change strength bidirectionally in response to spike timing (Verhoog et al., 2013). A study of connections between human pyramidal neurons and fast-spiking GABAergic interneurons reported four times more functional presynaptic release sites in humans compared with rodents (Molnár et al., 2016). Paralleling functional differences, dendritic spines from pyramidal neurons in the human brain were reported to be larger, longer, and more densely packed compared with their rodent counterparts (Benavides-Piccione et al., 2002). Moreover, astrocytes are known to play critical roles in synapse development, maintenance, and function (Allen et al., 2012;Allen and Eroglu, 2017;Bernardinelli et al., 2014;Di Castro et al., 2011;Krencik et al., 2017;Panatier et al., 2011;Ullian et al., 2001), and human astrocytes exhibit transcriptomic profiles as well as functional and structural properties that diverge from rodent astrocytes Oberheim et al., 2009). Thus, human and rodent astrocytes likely differ in their contributions to synaptic and neuronal function (Oberheim et al., 2009;Zhang et al., 2016). The above species-specific features highlight the importance of studying human neurons and human astrocytes to decode human nervous system function, which critically depends on networked neural activity involving both cell types.
Recent advances in human pluripotent stem cell (hPSC) biology and differentiation now allow for the generation of multiple human brain cell types in vitro including glutamatergic neurons (Busskamp et al., 2014;Chambers et al., 2009;Zhang et al., 2013). This has provided key insights into molecular mechanisms underlying normal synaptic development (Patzke et al., 2019), as well as alterations in human neurodevelopmental and neurological disorders such as autism (Krey et al., 2013;Marchetto et al., 2017;Yi et al., 2016), schizophrenia (Pak et al., 2015;Yoon et al., 2014), Alzheimer's disease (Israel et al., 2012;Lin et al., 2010), and Parkinson's disease (Sánchez-Danés et al., 2012;Vera et al., 2016). Advances in differentiation technologies have also facilitated the scalable production of hPSC-derived neurons to allow for phenotypic cell-based screening assays. Both genetic and pharmacological drug screening strategies using hPSC-derived neurons have been employed to study pathways regulating Tau and β-amyloid production in Alzheimer's disease (Brownjohn et al., 2017;Kondo et al., 2017;van der Kant et al., 2019;Wang et al., 2017). Other studies have used hPSCderived neurons to measure neurotoxicity (Ryan et al., 2016), neurite outgrowth (Sherman and Bang, 2018), and gene function using CRISPR interference and activation (Tian et al., 2021;Tian et al., 2019), further supporting the utility of such models for high-throughput screens. However, despite their unprecedented potential, scaled analyses of synaptic mechanisms using human cellular systems have not yet been achieved, in part due to the technical challenges associated with generating and analyzing reproducible culture preparations essential for these analyses. Indeed, while studies using human neurons have quantified the effects of candidate small molecules on synaptic density (Green et al., 2019;Patzke et al., 2019;Trujillo et al., 2021), to date we are not aware of any automated systems for their unbiased interrogation. This will become increasingly relevant for the study of basic synaptic mechanisms as additional species divergent features of brain cell types are identified and for the study of complex human diseases that impact synaptic function.
We therefore devised a fully automated and quantitative high-content synaptic phenotyping platform using in vitro-derived human glutamatergic neurons and primary human astrocytes. To demonstrate the utility of our platform, we screened effects of 376 small molecules on presynaptic density, neurite outgrowth, and cell viability. These analyses identified six small molecules that specifically enhanced presynaptic density. We then probed the most prominent hit class, bromodomain and extraterminal (BET) inhibitors, through global transcriptional analyses. While BET inhibitors have been associated with decreased synaptic gene expression in rodent models (Korb et al., 2015;Sullivan et al., 2015), we confirmed upregulation of synaptic gene expression in human cellular models, supporting the results of our synaptic assay. Taken together, we have established a novel human synaptic phenotyping platform that effectively identifies synaptic modulators in vitro. This platform can now be leveraged for further interrogation of human synaptic mechanisms and drug discovery efforts.

Generation of robust human neuronal cultures for synaptic screening applications
To reproducibly generate large batches of human-induced neurons (hNs) from hPSCs, we used a well-described in vitro differentiation protocol based on ectopic expression of Neurogenin 2 (NGN2) combined with developmental patterning using small molecules (Nehme et al., 2018;Zhang et al., 2013). We selected this protocol based on extensive molecular and physiological characterization of these hNs alongside their use in multiple studies of disease-associated genes (Busskamp et al., 2014;Deneault et al., 2018;Lin et al., 2018;Meijer et al., 2019;Nehme et al., 2018;Pak et al., 2015;Yi et al., 2016;Zhang et al., 2013;Zhang et al., 2018) and in the only published genomewide CRISPRi/CRISPRa screens of in vitro-derived human neurons to date (Tian et al., 2021;Tian et al., 2019). Importantly, studies using these hNs have identified physiological and/or morphological phenotypes upon perturbation of synaptic genes (e.g., NRXN1, SHANK3), which have then been recapitulated using other differentiation paradigms, further validating the accuracy of synaptic phenotypes detected in hNs generated with this protocol (Pak et al., 2015;Yi et al., 2016). Moreover, most neuronal differentiation paradigms in vitro generate heterogeneous cell types at lower throughput (Chambers et al., 2009) however, this protocol produces relatively homogeneous populations of glutamatergic neurons at large scale, making it uniquely suited for screening applications (Nehme et al., 2018;Tian et al., 2021;Tian et al., 2019). Data from our laboratory and others confirm that hNs begin to show electrophysiological activity around day 21 in vitro displaying spontaneous excitatory post-synaptic currents (sEPSCs) and NMDAR-mediated currents (Meijer et al., 2019;Nehme et al., 2018;Susco et al., 2020). However, it is important to note that at this timepoint synapses and synaptic network are still maturing.
To overcome the limitations of lentiviral titer used to transduce NGN2 and to enhance differentiation efficiency, we used a pair of TALENs to stably introduce a doxycycline-inducible NGN2 cassette  into the AAVS1 safe harbor locus of the hPSC line H1 to generate a zeocinresistant doxycycline-inducible NGN2 hPSC line, referred to as iNGN2-hPSC (Figure 1a), similar to the approach used by Meijer et al., 2019. Upon transfection, cells were selected for geneticin resistance and individual clones were isolated, expanded, re-plated for genomic DNA extraction and PCR Figure 1. Development of an automated, high-content human synaptic phenotyping platform. (a) Schematic of stable integration of a doxycyclineresponsive NGN2 (iNGN2) cassette into human pluripotent stem cells (hPSCs). TALENs were used to insert iNGN2 into the AAVS1 safe harbor locus of the PPP1R12C gene. (b) Summary of human-induced neuron (hN) protocol. hPSCs are differentiated into neural progenitor like cells using extrinsic inhibition of BMP, SMAD, and Wnt in combination with doxycycline-driven transient ectopic NGN2 expression. Zeocin is used as a selective agent. To generate large-scale neuronal preparations essential for screening applications, we then differentiated the iNGN2-hPSCs into hNs as described (Nehme et al., 2018;Figure 1b).

Establishment of automated pipelines for high-content human presynaptic phenotyping
We next developed a novel and scalable platform for automated quantification of presynaptic puncta in hNs co-cultured with human primary astrocytes (hpAs, ScienCell) ( Figure 1c). First, because the number and distribution of synapses in a network is critically dependent upon neuronal density (Cullen et al., 2010), we used an automated liquid handling system (Personal Pipettor, Apricot Designs) to maximize pipetting precision and accuracy and reliably dispense batches of post-mitotic hNs and hpAs across the 60 inner wells of each 96-well plate. Upon hN co-culture with hpAs, we observed a dense synaptic network ( Figure 1d). We specifically focused on the presynaptic marker SYNAPSIN1, which is crucial for maintenance, translocation, and exocytosis of synaptic vesicle pools, and the cytoskeletal protein MAP2 (microtubule-associated protein 2), which is primarily enriched in perikarya and dendrites (Caceres et al., 1984) in addition to DAPI-expressing nuclei. We then quantified presynaptic puncta, defined as SYNAPSIN1 puncta assembled on MAP2-expressing neuronal dendrites, which are (1) robustly captured in our human cellular models; (2) essential for normal neuronal development with perturbations implicated in disease (Südhof, 2017;Waites and Garner, 2011); (3) a prerequisite for the assembly of postsynaptic machinery (Friedman et al., 2000;Rohrbough et al., 2007;Sanes and Lichtman, 2001); and (4) successfully measured in multiple synaptic screens performed in mouse (Hempel et al., 2011;Spicer et al., 2018), as well as in small-scale synaptic measurements performed using human cellular systems (Chanda et al., 2019;Pak et al., 2015;Yi et al., 2016). SYNAPSIN1 appeared in a discrete punctate staining pattern localized on and along the clearly defined MAP2expressing neurites (Figure 1d). To enhance the resolution of presynaptic puncta and reduce nonspecific background for high-content imaging, we included glycine and TrueBlack Lipofuscin, key reagents for unmasking epitopes and quenching autofluorescence in our immunocytochemistry protocol.
In addition to quantifying SYNAPSIN1 on MAP2, we also assessed the possibility of incorporating an additional synaptic marker into our assay, testing conditions for labeling two additional presynaptic proteins: synaptophysin, SV2A, and four additional postsynaptic proteins: PSD-95, NLGN4, BAIAP2, and Homer1 (Figure 1-figure supplement 1 and data not shown). Among the tested antibodies, Neural progenitor-like cells were then incubated with neurotrophins in addition to the anti-mitotic agent floxuridine. hNs are then maintained in neurobasal medium and neurotrophins. (c) Flowchart of the high-content synaptic screening platform. One day after automated seeding of hNs and hpAs (using a liquid handler, day 5 of hN differentiation), a random plate is selected for assessing plating consistency (Columbus script). On day 18, treatments (shown here as small molecules at 3 µM) are administered in triplicate for 72 hr using a liquid handler; two plates are used as sentinel or reference. On day 21, co-cultures are then stained for synaptic markers using the liquid handler and high-content images are acquired using the Opera Phenix (Perkin Elmer). Data are then processed through CellProfiler and Screener to quantify the number and area of SYNAPSIN1 puncta on MAP2expressing neurites, the area of MAP2-positive neurites, the number of DAPI-positive nuclei, and the density of SYNAPSIN1 puncta on MAP2-expressing neurites (Z-score). (d) Representative images of hNs co-cultured with hpAs and stained for SYNAPSIN1 (red) and MAP2 (green). Cells are counterstained with DAPI (blue). Scale: 100 pixels. Insets: arrows show SYNAPSIN1 puncta localized on MAP2-positive neurites. (e, f) Acceptance criteria for plating consistency. Plating consistency for each batch is determined using a Columbus script by quantifying detected hN nuclei per well (e) as well as the intraplate covariance (f) of the randomly selected plate on day 6 after hN differentiation. Circles represented the number of neuronal nuclei detected per well from the plates randomly selected on day 6 (one day after seeding the co-culture). Thresholds for inclusion (red dashed lines) are set as above 4000 detected hN nuclei per well and below 12,000 detected hN nuclei per well (e) and a covariance below 8% across the plate (f). n = 1 x 96 well-plate per Batch which corresponds to 60 wells per 96-well plate per Batch. Error bars are shown as mean +/-SEM. (g) Quantification of the density of SYNAPSIN1 puncta on MAP2 neurites after incubation with SYNAPSIN siRNA versus control conditions. n = 6 wells for each condition; ***p<0.0001, ANOVA with Dunnett's post hoc test. Error bars are shown as SEM.
The online version of this article includes the following figure supplement(s) for figure 1:      we were able to quantify signal for synaptophysin, involved in synaptic vesicle endocytosis, and PSD-95, a key postsynaptic scaffolding protein (Figure 1-figure supplement 1). Roughly half of the SYNAPSIN1 signal on MAP2 colocalized with synaptophysin and vice versa (Figure 1-figure  supplement 1). Interestingly, studies in rodent cortex report that SYNAPSIN1 expression occurs prior to synaptophysin, potentially reflecting maturation of vesicle exocytosis mechanisms prior to endocytosis mechanisms (Pinto et al., 2013), which may contribute to the partial overlap in our assay. In general, SYNAPSIN1 is reported to be a more robust and specific marker for presynaptic terminals, with reports of synaptophysin also expressed at extrasynaptic sites (Micheva et al., 2010;Pinto et al., 2013). Indeed, the signal we obtained for synaptophysin was weak compared with the signal we obtained for SYNAPSIN1 (Figure 1-figure supplement 1). We observed moderate colocalization of PSD-95 with SYNAPSIN1; of the PSD-95 puncta on MAP2, 43.1% also colocalized with SYNAPSIN1 and of the SYNAPSIN1 puncta on MAP2, 28.8% colocalized with PSD-95 ( Figure 1-figure supplement 1). This may indicate presynaptic assembly occurring prior to postsynaptic assembly (Friedman et al., 2000;Rohrbough et al., 2007;Sanes and Lichtman, 2001) at this early stage of neuronal development. Again, at this developmental timepoint, the signal obtained for PSD-95 was weak and of insufficient quality as well as consistency for screening applications (Figure 1-figure supplement  1). It was therefore not feasible to incorporate these additional synaptic markers into our high-content assay; thus, we focused on SYNAPSIN1 puncta assembled on MAP2-expressing neurites.
To rigorously confirm hN plating consistency (1 d after co-culture, which corresponds to 6 d after the start of hN differentiation), we developed an in silico script using Columbus Acapella software (Perki-nElmer; Figure 1-figure supplement 2), quantified DAPI-expressing hN nuclei in the co-culture and established key thresholding criteria (i.e., the minimum number of detected hN nuclei per well and the related intra-plate covariance) for each batch of plates (Figure 1e and f). Of note, while several synaptic screens using mouse primary neurons report plating a range of 10,000-20,000 neurons per well in 96-well plates (Nieland et al., 2014;Sharma et al., 2012), we validated the number of hNs from a randomly selected plate 1 d after co-culture and found that 4000 surviving hNs per well was a minimum to establish a synaptic network and 12,000 surviving hNs per well was the maximum to circumvent hN clumping as well as reliably discriminate distinct SYNAPSIN1 puncta and track MAP2expressing neurites for image processing (Figure 1e). Importantly, consistency of plating was high within batches, with an 8% covariance threshold ( Figure 1f). Each treatment condition was compared to intra-plate/intra-batch controls and independent batches of plates were used as experimental replicates where treatment conditions were again compared to intra-plate/intra-batch controls.
Second, we developed an automated synaptic quantification pipeline called ALPAQAS (Automated Layered Profiling And Quantitative Analysis of Synaptogenesis) based on image analysis algorithms from the open-source software CellProfiler (Carpenter et al., 2006) to analyze our hN/hpA co-cultures after 21 d in vitro. We selected day 21 in vitro to study the developing synapse rather than a more mature synaptic network, which is not established in hNs at this timepoint. This decision was based in part on technical limitations associated with prolonged culturing of cells for screening applications, combined with the recognition that with current protocols, in vitro-derived neurons are unlikely to achieve a fully mature synaptic state regardless of time in culture. After immunostaining our co-cultures, image fields were acquired with a ×20 objective with the Opera Phenix high-content screening system (PerkinElmer), taking 12 fields per well and 5-8 stacks per field at 0.3 µm distance ( Figure 1-figure supplement 3). We then used ALPAQAS pipelines to quantify MAP2-positive neurites and SYNAPSIN1 puncta parameters based on staining intensity and morphological thresholding ( Figure 1-figure supplement 3). The first ALPAQAS pipeline was designed to merge in a single plane (maximum projection) the 5-8 stacks of each channel for each acquired field of a well. The generated output files were the inputs for a second pipeline, which corrected for potential illumination variations and translational misalignments, performed morphological reconstruction and de-clustering, and generated a *.csv file reporting at a field level: (1) the area covered by MAP2-positive neurites; (2) the number of SYNAPSIN1 puncta localized on MAP2-positive neurites; (3) the number of DAPI-positive nuclei; and (4) the area covered by SYNAPSIN1 puncta (Figure 1-figure supplement  3). To validate the specificity of synaptic detection in vitro, we assessed presynaptic density (i.e., the number of SYNAPSIN1 puncta localized on the MAP2-positive neurites divided by the area covered by MAP2-positive neurites) using ALPAQAS following small interfering RNA (siRNA) perturbation of SYNAPSIN1 (SYN1; Accell Dharmacon). Although siRNA-mediated knockdown is heterogeneous, incubating human co-cultures with SYN1 targeting siRNA for 72 hr significantly reduced presynaptic density compared to GAPD and non-targeting siRNA controls, with no or minimal impact on the area covered by MAP2-positive neurites or the total number of DAPI-positive neurites in any condition ( Figure 1g; Figure 1-figure supplement 4). SYNAPSIN1 puncta with diameters between 1 and 6 pixels were selected based on analyses of their cumulative distribution ( Figure 1-figure supplement  4), a setting which can be customized in our platform. Indeed, estimates of SYNAPSIN1 puncta diameter vary; some studies have used diameters of 0.4-1.6 µm 2 (Yi et al., 2016;Patzke et al., 2019), which would correspond to roughly 1-3 pixels in our assay ( Figure 1-figure supplement 4).
Third, to convert the field-level analysis generated by ALPAQAS to well-or condition-level analyses, and to control for potential intra-plate edge and drift effects, we used Genedata Screener software (Genedata). Specifically, the *.csv data file generated from ALPAQAS was imported into Genedata through a high-content parser containing three additional quality control steps: (1) data were selected according to their MAP2 field area coverage in comparison to the intra-batch mean MAP2 field area coverage; (2) data from wells of poor quality (fewer than five fields persisting after the first quality control step) were discarded from the analysis; and (3) a minimum of two out of three wells per condition (e.g., genotype, treatment) was required by the parser for use in Genedata Screener to aggregate the data from a well-level to a condition-level ( Figure 1-figure supplement 5). To control for potential intra-plate edge effects, we included two columns of control wells (0.1% DMSO treated) in each plate. In addition, two plates were assigned as sentinel or reference in each batch of human co-cultures to evaluate and correct eventual anomalies due to intra-batch drift effects. We then leveraged pattern correction algorithms in Genedata Screener to potentially rectify values across plates prior to performing Z-score calculations (Sherman and Bang, 2018;van der Kant et al., 2019;Zhang et al., 1999) for synaptic density ( Collectively, these customized protocols and analysis pipelines establish a novel platform for automated quantification of presynaptic density of hN + hpA co-cultures.
Primary screening results for 376 small molecules followed by secondary validation reveal six potent small molecules increasing presynaptic density To validate the robustness of our platform and uncover modulators of human synaptogenesis, we next assessed the impact of 376 small molecules from a highly selective inhibitor library (SelleckChem, L3500, Supplementary file 1). Here, small molecules were not selected based on known roles in neuronal or synaptic development, but instead from structurally diverse classes of inhibitors targeting kinases, chromatin modifiers, and cytoskeletal signaling pathways, among others (Supplementary file 1, Figure 2-figure supplement 1). Human co-cultures (hNs + hpAs) were treated with small molecules at a concentration of 3 µM in triplicate for 72 hr starting on day 18 of hN differentiation ( Figure 2a). Importantly, 92.94% of the control and treated wells had a Z-score for synaptic density between -2 and +2, underscoring the reliability and robustness of our automated high-content synaptic screening platform (Figure 2-figure supplement 1). Thirteen small molecules (3.46% of the library) did not meet the above quality control requirements and seven (1.86% of the library) reduced the density of SYNAPSIN1 puncta on MAP2 neurites by a Z-score ≤-3 and reduced the area covered by MAP2-positive neurites by a Z-score ≤ -2 ( Figure 2a and Figure 2-figure supplement 1); as expected, these small molecules were known to induce apoptosis or catalytic process of autophagy (Boccadoro et al., 2005;Piva et al., 2008;Reddy et al., 2011). Representative examples of the effects of individual small molecules on presynaptic density are shown in Figure 2b.
To further validate the top hit classes that increased presynaptic density in our primary screen, we executed secondary dose-response assays with newly purchased reagents. Specifically, we selected three heat-shock protein 90 inhibitors (Luminespib, Ganestespib, and Tanespimycin), three BET inhibitors (I-BET151 and (+)-JQ1 included in the primary screen in addition to Birabresib, which was added to expand the number of independent BET inhibitors queried), two phosphoinositide 3-kinase inhibitors (VS-5584 and TG100-15), two histone deacetylase inhibitors (Tubacin and Belinostat), two HMG CoA reductase inhibitors (Fluvastatin and Lovastatin), an S6 ribosome inhibitor and a ROCK1/ROCK2 inhibitor (BI-D1870 and GSK429286A, respectively), a histamine H1 and an opioid receptor antagonist (Loratadine and Naltrexone, respectively), and a ligand of ubiquitin E3 (Lenalidomide) (Figure 2a and  b), for a total of 17 small molecules. We then performed dose-response assays in hN + hpA co-cultures Figure 2. Primary screening results for 376 small molecules. (a) Z-score values for the density of SYNAPSIN1 on MAP2-expressing neurites for hN + hpA co-cultures incubated with small molecules in triplicate at 3 µM for 72 hr starting on day 18 of neuronal differentiation (each circle represents the small molecule level aggregate of the replicates). The effects of each individual small molecule were assessed using our in silico pipelines. Small molecules that increased presynaptic density and were selected for further validation are indicated by colored circles. Those in purple are anticarcinogens that Figure 2 continued on next page using the automated HP D300e Digital Dispenser (HP, FOL57A) to reliably and randomly dispense at increasing concentrations, nanoliters or microliters of the freshly dissolved small molecules across the 60 inner wells of each 96-well plate, and subsequently quantified synaptic density using our previously described ALPAQAS pipelines ( Figure 3a).
We found that 6 out of 17 small molecules tested exhibited a significant dose-dependent increase in presynaptic density compared to DMSO-treated controls ( Figure 3 and . Indeed, previous studies have shown that ROCK1/ROCK2 inhibition can enhance synapse formation in rodent models (Swanger et al., 2015), consistent with our results from GSK429286A treatment of hN + hpA co-cultures. Finally, VS-5584, an ATP-competitive Pi3K inhibitor with equivalent potency against all human isoforms (α, β, γ, and δ), drove a concentration-dependent increase in presynaptic density compared to the DMSO-treated wells, but not TG100-15, a selective inhibitor of the γ and δ isoforms (Figure 3d and For subsequent experiments, we determined the optimal dosage of each small molecule in hN + hpA co-cultures based upon (1) a significant increase in synaptic connectivity compared to the DMSOtreated wells; (2) a concentration higher than the EC (50); (3) minimal impact on the area covered by MAP2-positive neurites; and (4) minimal or no toxicity (Figure 3 and Figure 3-figure supplements 1-4). We selected 0.5 µM for (+)-JQ1, 1 µM for Birabresib, 3 µM for I-BET151, VS-5584, and BI-D1870, and 5 µM for GSK429286A as effective concentrations to increase human presynaptic density in hN + hpA co-cultures. We then derived hNs from an independent hPSC line with the same stable, inducible AAVS1 NGN2 integration strategy described above, and confirmed that four out of six small molecules, including all three BET inhibitors, increased presynaptic density in hN + hpA co-cultures at these specific concentrations (Figure 3-figure supplement 5), further validating our previous findings and indicating that the effects of the selected small molecules were generally reproducible across cell lines. Collectively, these analyses highlight the robustness and the sensitivity of our platform to detect modulators of human synaptogenesis.
Astrocytes are key regulators of human synapse assembly in vitro and response to small molecules Astrocytes are known to play critical roles in the regulation of neuronal network development Perez-Catalan et al., 2021), including the enhancement of presynaptic function (Ullian et al., 2001), and we therefore assessed the necessity of hpAs in mediating the effects of the identified small molecules. This was particularly relevant to assess, given that astrocytes are not standardly included in hN screening strategies. Specifically, we tested the impact of the six validated small molecules described above on presynaptic density in the absence of hpAs. hN monocultures were plated, treated, and analyzed using our established pipelines (Figure 4a). While hN monocultures passed all quality control and thresholding criteria (Figure 4-figure supplement 1), none of the six small molecules elicited a significant increase in presynaptic density in this condition (Figure 4b).
The online version of this article includes the following figure supplement(s) for figure 2:     As expected, the area covered by MAP2-positive neurites and the number of DAPI-positive nuclei remained unchanged by small molecule treatment (Figure 4c and d), paralleling the hN + hpA co-culture condition (Figure 3). Importantly, we performed experiments with and without hpAs in parallel using experiments with hpAs as a positive control for experiments performed without hpAs. Small molecule treatment also did not significantly affect the level of SYNAPSIN1 protein expression in either the hN monoculture or hN + hpA co-culture conditions as assessed by western blot analysis (Figure 4e and f), indicating that the differences observed between hN monocultures and hN + hpA co-cultures after small molecule addition were more likely due to changes in SYNAPSIN1 protein localization as opposed to overall protein abundance.
The addition of hpAs also had a significant effect on synapse development in the absence of small molecules, including a significant increase in SYNAPSIN1 presynaptic density as well as in the size of individual SYNAPSIN1 puncta with a modest reduction in the area covered by MAP2-positive neurites (Figure 5a-e). Indeed, the addition of hpAs roughly doubled both the density and size of SYNAPSIN1 puncta on MAP2-positive neurites compared with hNs alone (Figure 5c and d). Collectively, our results indicate that hNs can form quantifiable presynaptic puncta that can be reliably captured by our platform in the absence of astrocytes. However, hpAs contributed to the assembly or localization of presynaptic machinery, underscoring the relevance of astrocytes in human synaptic screening applications. hpAs were therefore included in all subsequent experiments.

Power calculations inform on study design
To more precisely define the power of our assay and gain additional insight into study design, we calculated effect size (Cohen's d) for our primary screen overall as well as for each small molecule tested, for our hN + hpA co-culture validation experiments, and for our hN monoculture validation experiments (Figure 5f and g, Figure 5-figure supplement 1, Supplementary file 2). For the primary screen, we obtained a Cohen's d of -0.18, as individual small molecules increased as well as decreased presynaptic density ( Figure 5-figure supplement 1, Supplementary file 2). Data for each small molecule included in the primary screen is shown in Supplementary file 2. Notably, both (+)-JQ1 and I-BET151 had very large effect sizes in the primary screen, with a Cohen's d of 2.914 for (+)-JQ1 and 3.710 for I-BET151 (Supplementary file 2). Consistent with the results of the primary screen, we also noted large effect sizes for each BETi tested in our hN + hpA co-culture validation experiments (Figure 5f and g, Figure 5-figure supplement 1, Supplementary file 2). Here, effect sizes were sufficiently large that the number of replicate fields (n = 47-80) and wells (n = 6-9) analyzed per small molecule could be scaled down in future experiments (Figure 5f and g, Figure 5-figure supplement 1, Supplementary file 2). For our hN monoculture validation experiments, we noted effect sizes in the small to medium range (Figure 5f and g, Figure 5- figure  supplement 1, Supplementary file 2). Here, we scaled up the number of fields (n = 404-417) and wells (n = 52) analyzed per small molecule, which was sufficient to achieve 95% power for detecting the small and medium effect sizes of Birabresib and I-BET151 (one-sided power = greater; Figure 5f and g, Figure 5-figure supplement 1, Supplementary file 2). These analyses confirmed that we were well powered to detect the impacts of BETi in both the hN co-culture and monoculture assays; however, the hN monoculture condition required substantially greater scale given the reduced effect sizes. Overall, in order to achieve 95% power (one-sided) to detect small effect sizes (Cohen's d = 0.2), experiments would require around n = 270 replicates, while medium effect sizes (Cohen's d = 0.5) would require around n = 43 replicates, and large effect sizes (Cohen's d = 0.8) would require around n = 17 replicates. In future experiments, small molecules with effect sizes similar to those of the BET inhibitors in the co-culture condition could be readily detected with modest experimental scale as noted above; in order to detect small effect sizes, experiments may require hundreds of replicates.

Multiple BET inhibitors enhance synaptic gene expression
Given that BET inhibitors were the most prominent hit class identified in our small molecule screen, with three independent BET inhibitors increasing presynaptic density in two independent hPSC lines, we sought to confirm a role for BET proteins in human synaptic development and further validate the results of our synaptic assay. The BET family of chromatin readers, which includes BRD2, BRD3, BRD4, and BRDT, bind acetylated lysine residues on histone proteins as well as transcription factors to mediate gene expression. Through unbiased screening, (+)-JQ1 was identified as a positive modulator of human neurogenesis . However, in adult mice, BET inhibition through (+)-JQ1 was shown to reduce synaptic gene expression and impair memory consolidation (Korb et al., 2015), while BET inhibition through I-BET858 in mouse primary neurons reportedly led to decreased expression of neuronal differentiation and synaptic genes (Sullivan et al., 2015). Importantly, regionspecific differences in response to (+)-JQ1 have been identified in the rodent brain, particularly in the context of dendritic spine density, suggesting that brain cell type may be a major determining factor in the expression of these phenotypes (Wang et al., 2021b). Moreover, a recent study found sex divergent effects of Brd4 on cellular and transcriptional phenotypes in both human and mouse (Kfoury et al., 2021), further supporting the hypothesis that the impacts of BET inhibition are highly context dependent.
We therefore performed global transcriptional analyses on both (+)-JQ1 and Birabresib-treated hN + hpA co-cultures compared with DMSO-treated controls (Figure 6a) to independently assess their roles in synaptic development. (+)-JQ1, Birabresib, or DMSO was added on day 18 in vitro for 72 hr, paralleling the approach used in our small molecule screen. Applying an adjusted p-value cut-off of 0.05 and log 2 fold change cut-offs of ≤-1 and ≥ 1, we identified a comparable number of significantly differentially expressed genes (DEGs) after (+)-JQ1 and Birabresib treatment compared to DMSO control (Figure 6b and c, Supplementary files 3 and 4). A majority of DEGs were downregulated after BET inhibition (Figure 6b and c), consistent with the known roles of BET proteins in transcriptional activation, and were also shared between the (+)-JQ1 and Birabresib treatment conditions (n = 2368; Figure 6d). We also observed strong positive correlation in the magnitudes of effect of (+)-JQ1 and Birabresib treatment as attested by the Pearson's r score of 0.9404 (Figure 6e). Given that both BET inhibitors increased presynaptic density (Figure 3b and c), we focused on the set of DEGs that were shared between (+)-JQ1 and Birabresib treatment conditions for the remainder of our transcriptional analyses. Using ingenuity pathway analysis (IPA), we confirmed that BRD4 (p=1.61 × 10 -17 ), (+)-JQ1 (p=0.000206), and Birabresib (p=4.59 × 10 -18 ) were all strongly predicted to be upstream regulators of the shared DEGs ( Figure 6-figure supplement 1). Signaling pathways such as 'DNA methylation and transcriptional repression' and 'axonal guidance' were enriched in the upregulated DEGs, while signaling pathways such as 'wound healing' and 'neuroinflammation' enriched in the downregulated DEGs (Figure 6f). We then leveraged SynGO (Koopmans et al., 2019) to specifically analyze synaptic ontology terms, focusing on genes upregulated in both the (+)-JQ1 and Birabresib transcriptional datasets (Figure 6g and h, Supplementary file 5). We found significant enrichment for the terms 'integral component of postsynaptic membrane' and 'postsynaptic membrane' with modest but significant enrichment for additional presynaptic terms (Figure 6g and h, Supplementary file 5) consistent with a role for BET proteins in the regulation of pre-and postsynaptic genes. Examples of individual pre-and postsynaptic genes upregulated following BET inhibition include the presynaptic cell adhesion molecule Neurexin 3 (NRXN3), the synaptic vesicle gene Amphiphysin (AMPH), the transmembrane Eph receptor ligand Ephrin-B2 (EFNB2) involved in AMPAR stabilization, and the postsynaptic scaffolding factors BAR/IMD Domain Containing Adaptor Protein 2 (BAIAP2) and Homer Scaffold Protein 1 (HOMER1) (Figure 6i). Indeed, (+)-JQ1 and Birabresib treatment each roughly doubled the expression of these pre-and postsynaptic genes. Additionally, western blot analysis revealed that both (+)-JQ1 and Birabresib significantly increased protein expression levels The online version of this article includes the following source data and figure supplement(s) for figure 4: Source data 1. Source of western blots for SYNAPSIN1 as well as GAPDH loading control following small molecule treatment versus DMSO control.     Consistent with the pathway analyses, we also observed modulation of the expression of several astrocyte-secreted factors including upregulation of the anti-inflammatory factor Interleukin 11 (IL11), downregulation of the pro-inflammatory factor Interleukin 6 (IL6), and downregulation of the synapse disassembly factor Secreted Protein Acidic And Cysteine Rich (SPARC) (Figure 6-figure supplement  1). Combined with downregulation of Caspase 1 (CASP1) and Gasdermin D (GSDMD) (Figure 6figure supplement 1), these results point to a general anti-inflammatory response following BET inhibition. Indeed, CASP1, GSDMD, and IL6 have all previously been shown to be downregulated by (+)-JQ1 treatment, consistent with the known role of BET proteins in promotion of the inflammatory response (Wang et al., 2021b;Zhou et al., 2019). Importantly, changes in gene transcription were again highly concordant between both (+)-JQ1 and Birabresib treatment conditions, with similar magnitudes of effect ( Figure 6-figure supplement 1).
Collectively, these data indicate that BET inhibition can lead to upregulation of synaptic genes, consistent with our screening results showing an increase in presynaptic density and supporting a role for BET proteins in synapse assembly. These results also support the robustness of our platform for detecting human synaptic modulators in vitro.

Discussion
Despite overwhelming evidence implicating synaptic dysfunction in complex human disease and clear species-specific differences in certain aspects of synaptic biology, human neurons and astrocytes have yet to be widely employed in studies of synaptic mechanisms. We therefore established a novel, automated, high-content synaptic platform using human neurons and astrocytes to facilitate dissection of human synaptic mechanisms and drug discovery efforts. Specifically, we (1) generated stable iNGN2-hPSC lines through TALEN editing to produce large, reproducible batches of postmitotic glutamatergic hNs; (2) optimized hN + hpA co-culture and immunocytochemistry conditions for reliable quantification of synaptic markers on an automated high-content imaging platform; (3) employed liquid handling systems to standardize the dispensing steps and maximize accuracy for both co-culturing and immunocytochemistry; and (4) developed in silico automated pipelines for rigorous assessment of plating consistency and quantification of human synaptic connectivity. After extensive vetting of antibodies for synapse detection, SYNAPSIN1 on MAP2 was the only antibody combination sufficiently robust to be captured in our assay. While there is no single definitive synaptic marker, SYNAPSIN1 is one of the most robust markers utilized in the field, having been shown to label the vast majority of rodent glutamatergic cortical synapses (Micheva et al., 2010) and frequently employed in studies of human cellular systems (Chanda et al., 2019;Pak et al., 2015;Yi et al., 2016). . The x-axis shows -log 10 of the p-value for each pathway analysis term. (g) SynGO analysis showing the top 5 significantly enriched cellular component and biological process ontology terms identified using all genes upregulated by (+)-JQ1 and Birabresib. The x-axis shows -log 10 of the p-value for each ontology term; the top two terms also met a<1% FDR threshold (h) SynGO analysis sunburst representation showing gene counts of the cellular components identified using all genes upregulated by (+)-JQ1 and Birabresib. (i) Graphs of transcript expression values for pre-and post-synaptic genes. Significance was calculated by Benjamini-Hochberg adjusted Wald test as part of the DEseq2 RNA-seq experiment. n=3 biological replicates. (j) Western blot analysis of BAIAP2 and Homer1 protein expression levels following treatment with DMSO, (+)-JQ1, or Birabresib (n = 4 biological replicates). Top, quantification (shown as % of DMSO control); bottom, example blots. For all figure panels, significance is indicated by *p<0.05, **p<0.01, ***p<0.001 relative to controls.
The online version of this article includes the following source data and figure supplement(s) for figure 6: Source data 1. Source of western blots for BAIAP2 and Homer1 as well as Cofilin loading control following DMSO, (+)-JQ1, or Birabresib treatment conditions.  The incomplete co-localization of SYNAPSIN1 with synaptophysin and PSD-95 could reflect factors such as developmental stage, as SYNAPSIN1 expression has been reported to precede synaptophysin and PSD-95 expression (Friedman et al., 2000;Pinto et al., 2013), or the weaker signal obtained by immunocytochemistry for the latter antibodies. Development of additional synaptic antibodies suited for immunocytochemical applications in human cells will be essential to more definitively address this question.
Of note, several open-source methods for synaptic quantification have been developed in recent years including SynQuant, SynapseJ, SynPAnal, and SynD (Danielson and Lee, 2014;Moreno Manrique et al., 2021;Schmitz et al., 2011;Wang et al., 2020) providing a solid foundation for the principle of synaptic quantification from immunofluorescence images. However, our platform is the first to use human neurons and human astrocytes for scaled analyses of synapses, and one of the few to fully automate each step of the process from co-culture to immunostaining to imaging, in order to enhance throughput, accuracy, and reproducibility. Through unbiased screening of 376 small molecules, we generated proof-of-concept data that our platform works robustly to identify synaptic modulators. Our results were also generally reproducible across independent hPSC lines. Given the known molecular and functional heterogeneity across individual hPSC lines (Kajiwara et al., 2012;Kilpinen et al., 2017;HIPSCI Consortium et al., 2018), we would not expect identical results or magnitudes of effect, but our analyses support the robustness of our assay to detect differences in independent hPSC lines.
Importantly, the results from hNs co-cultured with hpAs did not correlate with the results from hNs cultured alone, underscoring the importance of including astrocytes in human synaptic assays. Indeed, our positive hits required the presence of hpAs to achieve a significant change in presynaptic density, supporting the relevance of non-cell-autonomous factors in synapse assembly. Our power calculations comparing the hN monoculture condition with the hN + hpA co-culture condition suggest we are reasonably powered to detect changes in both assays given the predicted effect sizes. While we cannot rule out the possibility of technical factors contributing to the lack of changes in presynaptic density in the hN monocultures, as opposed to differences in the biological contribution of hpAs, it is critical to note for future screening applications that the inclusion of astrocytes was either required for these biological changes or important for their practical detection. Indeed, large effect changes seen in the co-culture condition could be detected with a small number of replicates (e.g., three wells), while the monoculture condition required 50+wells for the same small molecules. Consistent with astrocytes playing a critical role in synapse formation, upon hpA co-culture, we observed a twofold increase in presynaptic density and the area of individual SYNAPSIN1 puncta compared with hN monocultures. While previous studies have shown that inclusion of rodent astrocytes with hNs can significantly increase physiological and/or morphological maturation (Johnson et al., 2007;Nehme et al., 2018;Tang et al., 2013), the underlying mechanisms remain incompletely resolved. Studies examining the effects of physical astrocyte contact versus astrocyte conditioned media on hNs report that physical contact has a much more potent impact on synapse development compared with conditioned media (Johnson et al., 2007;Tang et al., 2013), suggesting that direct physical contact between hNs and hpAs may be particularly important. Our results imply that regulators of synapse formation may be missed in assays performed in the absence of astrocytes. Given that the inclusion of astrocytes more accurately mimics the in vivo synaptic milieu, and that astrocytes are not standardly included in hN screening strategies, it will be critical to consider this cell type in future human synaptic screening experiments.
Given that three separate BET inhibitors scored in our assay in two independent hPSC lines, our results also support a role for BET proteins in human synaptic development. While we were unable to separate hN and hpA transcriptomes from the co-culture (and the co-culture was required to see the effects of BET inhibition), we observed modulation of synaptic machinery in our global transcriptional analyses, consistent with our screening results. These data support a model whereby BET inhibition either leads to increased expression of specific synapse associated genes, or to the enhancement of presynaptic puncta which then triggers the expression of additional synaptic components. Indeed, BET protein expression is downregulated throughout neuronal differentiation, with knockdown of BET proteins sufficient to enhance differentiation  consistent with a role for BET proteins in negatively regulating neuronal development. These results contrast with a subset of data generated in adult mouse brain and are thus likely to be dependent upon factors such as brain cell type, developmental stage, and timing of drug treatment. Resolving potential neurological benefits and/or consequences of BET inhibition in different contexts including in diverse brain cell types and developmental stages is essential as these drugs are currently in use in clinical trials for cancer and are proposed for the treatment of developmental as well as degenerative diseases (Korb et al., 2015;Wang et al., 2021a).
While BET proteins have well-described roles in the inflammatory response, little is known about their role in astrocytes specifically. Future studies will be required to determine how BET inhibition impacts astrocytes and how this may in turn impact neuron function. Our results are consistent with BET inhibition enhancing synapse assembly and impacting both neuron and astrocyte transcriptomes, but it remains to be determined whether BET inhibition of astrocytes has a direct impact on synapse formation or whether BET inhibition primarily acts on neurons to impact synapse assembly. Overall, we speculate that hpAs provide critical synaptic support for hNs in our assay that then facilitates the health or receptiveness of hNs for further synaptic enhancement, given the disparities in SYNAPSIN1 parameters between hN monocultures and hN + hpA co-cultures, as well as the lack of effect of small molecules on hN monocultures.
Our robust, scalable and accessible platform provides several key advances for the field. Specifically: (1) we generated the first automated and quantitative high-content synaptic phenotyping platform for human neurons and astrocytes, which can now be leveraged for study of basic human synaptic mechanisms, dissection of complex human disease and drug discovery efforts; (2) our results demonstrate that the stable NGN2 integration strategy for scaled generation of hNs from hPSCs is well suited for screening applications; (3) our platform is compatible with both small molecule as well as siRNA-based manipulations; (4) our platform captures key parameters of human synaptic connectivity including the number and the area of individual presynaptic contacts as well as the coverage of MAP2-positive neurites and overall cell viability; (5) while compatible with analyses of hN monocultures, our analyses underscore the relevance of astrocytes when studying human synaptic mechanisms and drug response and (6) different human genetic backgrounds can be employed in our platform to dissect disease mechanisms. Future studies will focus on the incorporation of additional brain cell types to increase the complexity in our 2D co-culture systems, probing additional small molecule classes to explore novel human synaptogenic factors and improving the fidelity of astrocyte differentiation protocols in vitro to facilitate cell-type-specific genetic manipulations.
There are also several limitations to our approach: (1) our platform was specifically designed for use with human neurons and astrocytes and alterations to the neuronal differentiation paradigms or species, or the addition of other brain cell types may require modest customization steps; (2) our platform is based on high-content imaging, and neurophysiological function must be assessed using separate methodology, likely at lower throughput; (3) similarly, reliance on SYNAPSIN1 on MAP2 as a readout for presynaptic density in developing neurons may be capturing sites that do not then develop into fully functional/mature synapses with postsynaptic partners; (4) while the timepoints of manipulations and analyses can be altered in our assay, longitudinal studies in the same cells are not possible unless suitable genetically encoded fluorescent markers are employed; and (5) our platform is designed for 2D culture to allow for precise control over cell densities and ratios but does not fully reflect the structural complexity present in 3D systems (e.g., spheroids, organoids).

Genomic DNA isolation and genotyping PCR
Genomic DNA (gDNA) from the iNGN2-H1 cell line was extracted from hPSCs with the DNeasy Blood and Tissue kit according to the manufacturer's instructions (QIAGEN,69504). PCR of the gDNA was performed with the primer pair forward 5′-AGGA AATG GGGG TGTG TCAC -3′ (in the AAVS1 locus) and reverse 5′-GAGC TCCT CTGG CGAT TCTC -3′ (in the NGN2 DNA sequence).

Automated cell plating
hNs and hpAs were harvested with Accutase (Innovative Cell Technology, Inc, AT104-500), quenched in Neurobasal media, spun 5 min at 1000 rpm at room temperature (RT), passed through a 40 µm filter, and counted using the Countess Automated Cell Counter (Thermo Fisher Scientific, AMQAX1000). Cells were mixed in NBM media to reach a seeding density of 40,000 neurons. cm -2 and 100,000 astrocytes. cm -2 per well. A liquid handling dispenser (Personal Pipettor, ApricotDesigns) was used to uniformly plate the cell mixture in the geltrex-coated 60-inner wells of 96-well plates (PerkinElmer, CellCarrier-96, 6005558).

Small molecule dilution and addition
The Selleck library (L3500) consisted of 8 × 96-well plates containing 376 small molecules at 10 mM in DMSO (100%). On the day of small molecule addition, the relevant plate was diluted with an automatic liquid handling dispenser (ApricotDesigns, Personal Pipettor). Each diluted plate was screened on the same day, with each small molecule tested against the cell type of interest in three different 96-well plates (in triplicate) at 3 µM.

Immunocytochemistry
Immunofluorescence was performed using an automatic liquid handling dispenser (ApricotDesigns, Personal Pipettor

Image acquisition and analysis paradigms
Plating consistency 24 hr after co-culture, a random 96-well-plate was fixed and stained. 2 channels (DAPI, MAP2), 4 stacks. field -1 , each stack separated by 0.5 µm, 25 fields. well -1 , 60 wells.96-well plate -1 were acquired at ×20 (Plan Apo λ, NA 0.75, air objective, Nikon) with a high-content screening confocal microscope (ImageXpress Micro 4, Molecular Devices). Acquired images were transferred and stored on a Columbus (PerkinElmer) server. A Columbus Acapella software (PerkinElmer) algorithm was designed to discriminate hN nuclei from hpA nuclei. DAPI and MAP2 channels of each field were merged in a single plane to create maximum projection images. DAPI projected images were filtered using a Gaussian method and the nuclei population was detected based on the C method (diameter >20 µm). Intensity and morphological properties of each identified nuclei were calculated. Among all nuclei, the hN nuclei were identified according to their intensity, contrast, as well as area, roundness, and distinct MAP2-positive soma (Figure 1-figure supplement 2). This script allowed quantification of the mean number (and the standard deviation) of hNs identified in each well, and the coefficient of variance (%) per plate. Thresholding for plating consistency was set above 4000 hNs per well and a covariance below 8% per plate.

Synapse detection
Three channels (DAPI, MAP2, SYNAPSIN1), 5-8 stacks. field -1 at 0.3 µm distance, 12 fields. well -1 , 60 wells.96-well plate -1 were acquired with a ×20 objective (NA 1.0, water objective) with a high-content screening confocal microscope (Opera Phenix, PerkinElmer). Image analysis pipelines were built using the open-source CellProfiler 3.1.5 software (https://www.cellprofiler.org/; Carpenter et al., 2006;RRID:SCR_007358). Each of the 12 fields per well were analyzed independently. The first pipeline was designed to merge in a single plane (maximum projection) the 5-8 stacks of the raw image for each channel. The output files generated by the first pipeline were the input files for a second pipeline. The second pipeline was developed to report different features in MAP2 and SYNAPSIN1 channels. First, translational misalignment was corrected by maximizing the mutual information of the DAPI and MAP2 channels. The same alignment measurements obtained from the first two input images were applied for the SYNAPSIN1 channel. Illumination correction functions were created by averaging each pixel intensity of all images from each channel across each plate then smoothing the resulting image with a Gaussian filter and a large filter size of 100 × 100 pixels. A rescale intensity function was used to stretch each image of each channel to the full intensity range (so that the minimum intensity value had an intensity of zero and the maximum had an intensity of 1). The MAP2 pixel intensities were enhanced by a tophat Tubeness filter with a scale of 2 sigma of the Gaussian. Once the lower and upper bounds of the three-class Otsu thresholding method were set for the MAP2 pixel detection, the resulting images were converted into segmented objects. The SYNAPSIN1 pixels were enhanced using a tophat Speckles filter, thresholded using a two-class Otsu method, processed for SYNAPSIN1 puncta identification within an equivalent diameter range of 1-6 pixels, and de-clumped before being converted into segmented objects. A colocalization module then assigned the relationships between the identified SYNAPSIN1 puncta contained within or only partly touching the MAP2 objects. Finally, the area and the number of the synaptic objects were measured and exported to a *.csv spreadsheet. The CellProfiler pipelines are available at https://github.com/mberryer/ALPAQAS (copy archived at Berryer, 2023).

Data analysis Quality control
The output CellProfiler *.csv files were imported in Genedata Screener. As the *.csv files were loaded, a parameterized parser conducted quality control. The mean and standard deviation of the area covered by MAP2 pixels were calculated across all fields in an inter-plate basis. Fields in which the area covered by MAP2 pixels was outside the range of the inter-plate mean ± standard deviation values were removed. Wells containing fewer than five fields were also excluded. The area and the number of the synaptic objects of the remaining fields were averaged and condensed to well-level before being imported into Genedata Screener to perform relevant calculations.

Synaptic measurements
We analyzed the area occupied by MAP2-positive neurites and the number, area, and density of SYNAPSIN1 puncta localized on MAP2-positive neurites (respectively AreaOccupied and Count of MAP2PositiveNeurites and SYNAPSIN1PunctaOnMAP2PositiveNeurites in the ALPAQAS pipeline). On a well-basis, density was defined as the number of SYNAPSIN1-positive puncta colocalized on MAP2-positive pixels divided by the area covered by the MAP2 positive pixels. The mean values for number and density were calculated for each well and expressed as a percentage of the intraplate DMSO control-treated wells. Computed on an intra-plate basis, the Z-score for the value X was defined as the difference between X and the mean value of all DMSO-treated wells, divided by the standard deviation value of all DMSO-treated wells.

SynGO analysis
Individual upregulated DEGs shared between (+)-JQ1 and Birabresib treatment conditions (p adj <0.05 and log 2 FC≥1) were used as input with default settings. All genes with average counts ≥1 in the 0.1% DMSO control conditions were used as the background list (for a total of 11,704 genes).

Power calculations
Cohen's d was calculated for each compound compared to DMSO using pooled standard deviation. The statistical power of each compound was calculated from Cohen's d, number of fields/wells, with alpha 0.05, hypothesis as specified ('two-sided,' 'greater,' or 'less') using the function pwr. norm. test from R package pwr (1.3-0). The number of fields or wells was calculated for increasing Cohen's d and power, with alpha 0.05, using the function pwr. norm. test from R package pwr (1.3-0). Power plots were generated with ggplot2 (3.3.5).

Statistical analyses
All experiments were performed in three biological replicates and three technical replicates, except the small molecule validation experiments were performed in two biological replicates and eight technical replicates and the siRNA experiment was performed in one biological replicate and six technical replicates. Biological replicates refer to independent batches of cells/differentiations, and technical replicates refer to independent wells. For the dose-response, the small molecule validation, the hN monoculture experiments, and the immunoblot quantification, significance was assessed using one-way ANOVA with Dunnett's multiple comparisons test; *p<0.05, **p<0.01, ***p<0.001. In the hN + hpA co-culture versus hN monoculture experiment, the quantification of the density and the area of the individual SYNAPSIN1 puncta and the area covered by MAP2-positive neurites, the significance was determined by a Kolmogorov-Smirnov unpaired t-test, ***p<0.001. For mRNA-seq analyses, we used an adjusted p-value cutoff of 0.05 and a log 2 fold change cutoff of ±1. p-Values (or adjusted p-values where relevant) 0.05 were considered statistically significant.

Additional information
Competing interests Lee L Rubin: Reviewing editor, eLife. LLR is a founder of Elevian, Rejuveron, and Vesalius Therapeutics, a member of their scientific advisory boards and a private equity shareholder. All are interested in formulating approaches intended to treat diseases of the nervous system and other tissues. He is also on the advisory board of Alkahest, a Grifols company, focused on the plasma proteome and brain aging. None of these companies provided any financial support for the work in this paper. The other authors declare that no competing interests exist.

Additional files
Supplementary files • Supplementary file 1. List of the 376 small molecules used in the primary screen including molecule name, synonyms, library, supplier, catalog number, target, additional notes, and pathway.
• Supplementary file 2. Power calculations for the primary screen overall, the primary screen by compound, co-cultures at the field level, co-cultures at the well level, neuron monocultures at the field level, and neuron monocultures at the well level.
• Supplementary file 5. SynGO analyses including the GO term ID, domain, name, and p-value.

• MDAR checklist
Data availability Data, resource and code availability: All data is available in the manuscript or the supplemental materials. The analysis codes generated in this study are available from GitHub at: https://github. com/mberryer/ALPAQAS (copy archived at Berryer, 2023). Engineered cell lines are available upon request and following appropriate institutional guidelines for their use and distribution.